Current display architectures for search results display the results in a “long list” format. The results are generally displayed in an order based exclusively on relevance criteria. Typically, these relevance criteria are associated with two concepts related to the relationship between a result and the query for which the result is produced. The first concept for determining relevancy is the frequency with which the search terms appear in the indexed document. The second concept for determining relevancy is the proximity of individual search terms within the document. Using these relevance criteria, display systems then calculate the order in which results are presented are based on some mathematical interrelation between the frequency measurement and the proximity measurement. Displaying search results in this manner does not provide any information on the source of the indexed document, which the user may find relevant to the applicability of the search result to the considerations driving his or her need for the underlying information. Sources, by way of non-limiting example, include the particular database or search engine from which a result was extracted or may mean the originating party or parties that created a document.
While current display algorithms are effective at displaying items for presentation in terms of their relevance to the terms of a particular search query and the frequency and proximity of the query terms, current search algorithms are ineffective at displaying items in terms of the relevance to the user who created the query, which may depend strongly on the source of the data. Ultimately, the accuracy of a frequency and proximity measurement is not a useful proxy for search effectiveness. The point of a search is to display quickly to the user results that are relevant to the purpose for which the user who entered the query, and current search algorithms perform suboptimally in that regard to the extent that a source of data determines applicability to the needs of the user.
Additionally, because the references are displayed in a long list with data from different sources comingled in the list, it is frequently the case that the results most applicable to the user's needs are buried far down in a page of results. Results from a valuable source are often buried beneath or interspersed between results from sources that are not particularly useful. Such a situation can occur because the particular source for which the user is searching performs poorly on the frequency and proximity metrics or has only a few relevant result candidates. There is a significant cost associated with search results that are displayed in an order that is irrelevant to the user who created the search. That cost is measured in terms of time lost by the user in manually searching a long list or re-running searches. Searches are re-run with added terms to attempt to increase the display prominence of results from a particular source, for instance when the search results that are displayed first are relevant to the terms of the query but are from sources that are irrelevant to the user. That cost is measured in terms of time lost by the user in scrolling through multiple search results in an attempt to find results that are relevant to the user's needs. Multiplied across the millions of annual searches performed by an enterprise with thousands of employees, that time cost is a significant drag on organizational productivity.